Pharmacophore Model Development for the Identification of Novel Acetylcholinesterase Inhibitors Edwin Kamau Dept Chem & Biochem Kennesa State Uni ersit Kennesa GA 30144 Dept. Chem. & Biochem. Kennesaw State University, Kennesaw, GA, 30144. Bioengineering & Bioinformatics Summer Institute, Dept. Computational Biology, University of Pittsburgh, PGH, PA 15260.
Background Acetylcholinesterase Acetylcholinesterase(AChE) is a multifunctional enzyme found in cholinergic system Function: Terminates nerve impulses of acetylcholine origin Promotes production of β-amyloid protein Associated with pathogenesis of Alzheimer's Disease (AD) Impairs cholinergic system Reduced amount of acetylcholine Promote formation of Plaques Insoluble β-amyloid protein http://www.cnsforum.com com
Alzheimer's Disease Age related chronic neurodegenerative dementia Affects 20 million people worldwide Develops in stages Early to advanced Available drugs only able to treat certain stage only Drugs have associated side effects Need for therapeuticti measures with ability to treatt all stages of AD with less or no side effects
Structure Model structure of Torpedo californica AChE (TcAChE) Peripheral Anionic Site (PAS) Trp 279 Catalytic Anionic Site (CAS) Trp 84 Lining of aromatic residues 54 X-ray structures with bound ligands Xu. et al. Induced fit or preexisting equilibrium dynamics? 2008,17:601-5
Inhibition Binding activity at CAS well understood while that of PAS still elusive PAS site associated with formation of plaques Treatment at all stages of AD ineffective due to drug inhibition only at the CAS site Inhibition at both sites is key to effective treatment Hence using available generated dual-inhibitors of TcAChE will generate more effective inhibitors
Aims of this Study Develop a molecule-derived pharmacophore model (MOE) using known PAS and CAS inhibitors Use pharmacophore p model to identify structurally diverse active compounds by virtual screening
Strategy (LBDD) Start (SBDD) TcAChE Training set Top 5 Hits Pharmacophore Model Development Experimental testing (TBD) Pharmacophore Model Validation Lipinski`s Rule of Five Criteria 3D Database screening Top 25 Hits
ACTIVE DUAL INHIBITORS
Training set
Pharmacophore Query Generation Training set: 6 compounds Features defined: Hydrogen-bond acceptor (ACC) Hydrogen-bond donor (DON) Hydrophobic (HYD) Aromatic (ARO) Conformer generation 4 kcal/mol l
Queries 3 Queries generated: Query Molecule(s) Tolerance Radius Features a ReF_Vol Donepezil 1.74 HYD ARO 3Vols Ref_No _Vol Donepezil 1.4 HYD ARO Consensus All training set molecules 1.4 HYD ACC DON ARO. a ACC,hydrogen-bond acceptor; DON, hydrogen-bond donor; HYD, hydrophobic, ARO, ring aromatic, Vols, excluded volumes
Reference Queries Reference query without excluded volumes generated using Donepezil the meshed spheres represent pharmacophoric features. TRP 279 TRP84 PHE330 Reference query with excluded volumes generated using Donepezil. The meshed spheres are the pharmacophore features (ARO, HYD). Solid spheres (red, blue and cyan colored) represents excluded volumes.
Consensus Query Bis5 tacrine Bis10 Hupyridone Aminoquinoline28 Bw284c51 Decamethonium Donepezil Fig 4: Flexible aligned training set showing defined features scheme used to generate the query Consensus query generated from flexible aligned training set. The spheres are the pharmacophore features (Aro, Acc, Hyd, Don) that novel pharmacophores were aligned. Molecules matching those features satisfied the query and were therefore hits
Validation TEST SET: 8 active compounds All 8 molecules satisfied the three queries
Database Preparation & Search (MOE) ZINC ~ 2.8 M total number of compounds ~ 900,000 lead-like compounds 27,491 compounds screened Created conformation database: 703,313 compounds
Database Search Results and Sorting Query Ref:Vol Ref:No Vol Consensus Number of hits 211 378 329 Sort top 100 100 100 100 Common hits 68 Selection based on lowest rmsd value (0.06-0.3 Å) 25
Sorted Hits (cont.) Consensus compounds generated by all queries and their respective RMSD values.
Sorted Hits (2D Structures)
Virtual Screening Flexible Docking with Molegro Virtal Docker Full side-chain flexibility Cavity detection dynamically used during the docking process Improved scoring function hydrogen bond directionality taken into account Table 1: Accuracy of selected docking programs. A binding mode is regarded as correctly identified if the RMSD (to the native co-crystallized ligand) is less than 2.0Å. The dataset consists of the 77 complexes *Thomsen R, Christensen M H, J Med Chem 2006; 49:3315-3321
Docking Results Top 5 ZINC compounds docked in the binding sites of TcAChE
Docking Results (cont.) E2020 (Donepezil) and ZINC 3785268 from different view points. Visual of how well the zinc compounds aligned in the binding site similar to Donepezil
Conclusion Using a combination of ligand based and structure based drug design approaches we have identified structurally diverse dual inhibitor candidates for TcAChE Future Work Virtually screen the remaining lead-like like compounds of the ZINC database Prioritize hits for experimental testing (i.e. binding free energy; types of interactions in the active site)
Acknowledgements Dr Gabriela Mustata Dr Jeffry Madura BBSI Program NIH & NSF
References 1. Lin A. Overview of pharmacophore p Application in MOE. Chemica Computing group. http://www.chemcomp.com/journalcom/journal /ph4.htmhtm 2. Irwin and Stoichet. J. Chem. (2005); 45(1): 177-182 182 Info. 3. Molegro Virtual Docker. www.molegro molegro.com 4. Xu Model. Xu. et al. induced fit or preexisting equilibrium dynamics? Protein Science (2008), 17: 601-5